Yapay Zeka Hızlı Bakış & Çıkarımlar
AI InsightIncorporating these markers into multifactorial models may improve pre-transplant risk stratification.
📌 3 Maddede Bu Makale
Abstract
Aims: Patients undergoing allogeneic hematopoietic stem cell transplantation (allo-HSCT) are highly susceptible to infectious complications due to immunosuppression and delayed immune reconstitution, with pneumonia remaining a major cause of early morbidity and mortality. Composite inflammation- and protein-based biomarkers, including the C-reactive protein-to-albumin ratio (CAR) and fibrinogen-to-albumin ratio (FAR), have prognostic value in oncology, but their role in predicting pneumonia and survival after allo-HSCT is unclear. Methods: This retrospective, single-center study included 203 adult allo-HSCT recipients treated between 2020 and 2023. Pre transplant inflammatory and protein-based biomarkers were assessed 7-14 days before transplantation, and Cox proportional hazards regression was used to identify independent predictors of one-year post-transplant pneumonia and mortality. Results: Pneumonia occurred in 17.7% of patients, and one-year mortality was 41.9%. Although lower albumin, hemoglobin, and platelet levels and higher procalcitonin (PCT) concentrations were associated with pneumonia in univariate analyses, no biomarker independently predicted post-transplant pneumonia after multivariate adjustment. In contrast, pre-transplant PCT (HR=1.020, p<0.001) and total protein (HR=0.929, p=0.002), along with male sex and underlying diagnosis, independently predicted one-year mortality. CAR and FAR were associated with mortality only in univariate analyses. Conclusion: Pre-transplant inflammatory and protein-based biomarkers reflect biological vulnerability in allo-HSCT recipients. Although none independently predicted post-transplant pneumonia, PCT and total protein predicted one-year mortality, suggesting that systemic inflammatory burden and baseline protein status are more informative for long-term prognosis than for infection risk. Incorporating these markers into multifactorial models may improve pre-transplant risk stratification.